UniRS-Instruct: A Principle-Guided Unified Instruction-Following Dataset for Remote Sensing Understanding

Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models

The results suggest that competitive MLLM can emerge from alignment alone, reducing multimodal extension to a lightweight projector-training problem that generalizes across modalities and adapts rapidly to each new LLM release.

Vision-Language Models: Bridging Linguistic and Visual Understanding – A Case Study of IndoAI

An overview of Vision LLM architectures, their applications and the challenges they face and case study of how building of AI Models through visionLLM may help IndoAI AI camera system are provided.

Unified Sequence Modeling for Remote Sensing: A Parameter-Efficient Foundation Model via Prompt-Driven Granularity Alignment

Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild

Human-object interaction detection (HOID) has traditionally been formulated as a supervised detection problem over predefined interaction categories. While such paradigms achieve strong performance on closed-set benchmarks, they fundamentally entangle interaction understanding with dataset-specific supervision, limiting their ability to generalize to open-world and compositional scenarios. Recent HOI detectors attempt to leverage MLLMs through prompting strategies to transfer interaction-specific knowledge. However, such prompt-based approaches primarily focus on extracting discriminative representations from pretrained models, while underexploring their inherent multimodal reasoning capabilities. As a result, they struggle to provide informative contextual reasoning for ambiguous and open-world interaction scenarios. In this work, we present AgentHOI, a training-free, agentic framework that transfers the generalist multimodal reasoning capabilities of foundation models to HOI detection in the wild. Instead of learning interaction classifiers, AgentHOI modularly orchestrates complementary vision foundation modules to perform open-ended semantic reasoning and spatial grounding in a coordinated manner. To address the challenges of incomplete interaction discovery and ambiguous localization in complex scenes, we introduce two key mechanisms: (1) Context-aware Multi-round Reasoning, which progressively refines interaction hypotheses to ensure exhaustive and compositional HOI discovery, and (2) Multifaceted Interaction Localization, which enhances grounding precision by generating instance-specific descriptions that integrate semantic, spatial, and appearance cues. Extensive experiments demonstrate that AgentHOI achieves superior performance over state-of-the-art supervised and weakly supervised methods in real-world settings, despite requiring no HOID data for training.

Knowledge-Guided Vision-Language Models Prompt Learning for Open-Vocabulary Remote Sensing Image Scene Classification

Remote sensing open-vocabulary scene classification aims to recognize unseen scene categories beyond the predefined training set by leveraging external knowledge and vision-language models. Prompt learning has emerged as an effective paradigm to adapt pretrained vision-language models to open-vocabulary remote sensing image processing tasks by dynamically encoding task-specific knowledge into textual prompts. However, most existing approaches mainly rely on category names derived from training data, which lack fine-grained semantic descriptions and attribute-level associations that are crucial for complex remote sensing scenes. This limitation often degrades generalization when encountering novel classes and diverse geospatial domains. To address this problem, we propose KnowProKD, a knowledge-guided framework that incorporates diverse external knowledge and constraint-based regularization to enhance base-to-novel generalization. Specifically, KnowProKD consists of three components: 1) leveraging large language models (LLMs) to generate diverse attribute and description knowledge for constructing knowledge-aware prompts, improving cross-modal semantic alignment; 2) introducing knowledge-based constraints to regularize training and enhance robustness to novel category recognition; and 3) designing a dual-branch logits calibration mechanism that exploits attribute and description prompts to balance performance across base and novel classes. Extensive experiments on eight challenging remote sensing scene classification benchmarks demonstrate that KnowProKD achieves favorable performance compared with recent prompt-learning baselines and generalizes well to previously unseen scenarios. Furthermore, we apply KnowProKD to cross-regional urban land-use mapping and statistical analysis across representative cities from six continents, validating its practical effectiveness in real-world remote sensing applications.

Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?

The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU), enabling natural-language interaction with remote sensing imagery. However, a systematic understanding of the capability boundaries, cross-task generalization, and task-specific limitations of existing remote sensing MLLMs (RS-MLLMs) is still lacking. This paper presents a systematic survey and diagnostic evaluation of MLLMs for RSISU. We review the technical evolution of RS-MLLMs, focusing on model design, multimodal learning, training data, and downstream capabilities. We further compare RS-MLLMs with general-purpose computer vision MLLMs (CV-MLLMs) across diverse RSISU tasks and benchmarks. RS-MLLMs remain competitive in domain-specific settings, particularly remote sensing visual grounding and high-resolution visual question answering. More notably, general-purpose CV-MLLMs can match or even outperform these specialized models on several RSISU tasks without remote sensing-specific fine-tuning. These findings demonstrate the strong transferability of general-purpose CV-MLLMs and show that current RS-MLLMs do not consistently outperform them across diverse RSISU tasks. Current MLLMs also face limitations in spatial and relational reasoning, fine-grained visual understanding, instruction diversity, and generalization across heterogeneous task formats. Based on these findings, we outline future directions toward reliable evaluation, multimodal and high-resolution reasoning, efficient deployment, and tool-augmented remote sensing agents. This survey provides a systematic reference for developing robust, generalizable, and practical MLLMs for RSISU.